This invention relates generally to a load relief system for a launch vehicle and more particularly to a load relief system for a launch vehicle which measures the winds in near real time and provides control commands that compensate for wind load on the launch vehicle by reducing the total angle of attack.
The decision whether to proceed with the launch of vehicles such as rockets, missiles, the Space Shuttle, and the like, is determined in part by the prevailing wind conditions at altitudes up to approximately thirty kilometers above the Earth. Strong winds at altitudes up to approximately 30 kilometers an impart significant bending loads on the launch vehicle during ascent and hence often result in a decision to scrub the launch. Scrubbed launches are expensive and can increase the cost of the launch by as much as five hundred thousand dollars per day.
Conventional systems for reducing the load on a launch vehicle during ascent rely on balloon wind measurement systems, such as jimsphere or rawindsonde balloons, to determine the wind speed and direction at high altitudes. These balloon systems are generally released every hour up to six hours before the scheduled liftoff, and drift with the prevailing wind. The movement or drift of the balloon is generally measured with a ground based radar tracking system. However, because of the associated wind drift of the balloons, these wind measurement systems do not measure the wind speed and direction along the actual expected flight path of the vehicle. The balloon based systems are also unreliable because of loss of radar tracking and mechanical balloon failure. Moreover, the prior art balloon systems generally require approximately sixty to ninety minutes to reach their peak altitude (e.g., 30 kilometers). These systems also require additional time to post process the radar tracking data to estimate and determine the wind profile for launch. Once the wind profile has been determined, it is typically stored and used during the flight of the launch vehicle by an on-board flight processor to reduce the effects of those measured winds during ascent. However, because wind speed and direction are constantly changing, the actual winds experienced by the launch vehicle during flight can be significantly different from those measured prior to flight.
Present systems for reducing the load on a launch vehicle use the previously determined wind profile (e.g., 60 to 90 minutes old at the time of launch of the vehicle) in a largely reactive traditional feedback guidance and control system. Typical guidance and control systems modulate the attitude of the vehicle to provide load relief during the launch vehicle ascent. These guidance and control systems generally employ polynomial curve fits of the measured wind profile and utilize traditional control loops with additional load relief compensation to reduce the loads on the launch vehicle. However, polynomial curve fits of the wind profile tend to average or filter out the effects of rapidly changing winds such as wind shears. Moreover, the prior art load relief systems tend to be reactive in nature. In this regard, during vehicle ascent, an on-board inertial measurement unit (IMU) requires time to detect any actual and/or uncompensated wind shear. Due to this time delay, the majority of the load due to the wind shear has already been transferred to the launch vehicle.
It is therefore an object of this invention to provide an improved load relief system for a launch vehicle.
It is a further object of this invention to provide such a load relief system in which the wind sensor senses the wind speed and direction approximately every three minutes at altitudes up to approximately thirty kilometers above the Earth.
It is a further object of this invention to provide such a load relief system in which the wind sensor senses the wind speed and direction in the range of about every one minute to about every fifteen minutes at altitudes up to approximately thirty kilometers above the Earth.
It is a further object of this invention to provide such a load relief system in which the wind speed and direction provided reflect the actual prevailing wind conditions experienced during the flight of the launch vehicle.
It is a further object of this invention to provide such a load relief system which efficiently reduces aerodynamic loads on the launch vehicle during flight.
It is a further object of this invention to provide such a load relief system which accurately predicts a trajectory path of the launch vehicle which reduces the load experienced by the launch vehicle by comparing the predicted trajectory to a desired trajectory.
It is a further object of this invention to provide such a load relief system which efficiently provides load relief to the launch vehicle when the vehicle is subjected to wind shear.
It is a further object of this invention to provide such a load relief system which efficiently provides control commands which compensate for wind induced loads on the launch vehicle by reducing the total angle of attack of the launch vehicle.
It is a further object of this invention to provide such a load relief system which reduces the number of scrubbed launches.
The invention results from the realization that a truly effective and robust load relief system for a launch vehicle can be achieved by a unique combination of a wind sensing system responsive to atmospheric winds for providing an output of sensed wind speed and direction at selected locations; a plant model responsive to the sensed wind speed and direction within a finite time horizon, the current state of the launch vehicle, and control commands of the launch vehicle to predict the trajectory of the launch vehicle; an error circuit responsive to the predicted trajectory and a reference trajectory to produce a trajectory error, and an optimizer responsive to the trajectory error and configured to provide control commands that compensate for wind loads over the finite horizon of the launch vehicle by reducing the total angle of attack.
This invention features a load relief system for a launch vehicle including a wind sensing system responsive to wind speed and direction at selected locations for providing an output of sensed wind speed and wind direction at the selected locations, a plant model responsive to the sensed wind speed and direction within a finite horizon, a current state of the launch vehicle, and control commands of the launch vehicle to predict the trajectory of the launch vehicle, an error circuit responsive to the predicted trajectory and a reference trajectory to produce a trajectory error, and an optimizer responsive to the trajectory error and configured to provide control commands to compensate for wind load over the finite horizon of the launch vehicle by reducing the total angle of attack.
In a preferred embodiment, the wind sensing system may sense the wind speed and wind direction at altitudes up to approximately 30 kilometers. The wind sensing system may be mounted on the launch vehicle. The wind sensing system may sense the wind speed and wind direction approximately in real-time. The wind sensing system may be located proximate a launch platform of the launch vehicle and may sense wind speed and direction at least approximately every three minutes or in the range of about every one minute to every fifteen minutes. The wind sensing system may be located on an aircraft and may sense wind speed and direction at least approximately every three minutes or in the range of about every one minute to every fifteen minutes. The launch vehicle may be chosen from the group consisting of Delta, Atlas, Arian, Titan, and Space Shuttle. The wind sensing system may include a Doppler Light Detection and Ranging (LIDAR) sensor. The wind sensing system may include a radar system. The LIDAR sensor may emit an eye safe energy beam pulse. The load relief system of this invention may further include a wind correlator, responsive to the wind sensing system and the current state of the launch vehicle for determining a vector of wind speed and wind direction over the finite horizon of the launch vehicle as a function of time. The vehicle state may include a position vector and a velocity vector. The vehicle state may further include an acceleration vector. The wind correlator may determine the vector of wind speed and wind direction over the finite horizon of the launch vehicle as a function of time by propagating the current vehicle state based upon numerical integration of Newtonian equations of motion. The wind correlator may determine the vector of wind speed and wind direction over the finite horizon of the launch vehicle as a function of time by determining the vehicle altitude as a function of time over the finite horizon based upon the propagated state of the launch vehicle. The plant model may predict the trajectory of the launch vehicle over the finite horizon by integrating Newtonian equations of motion. The plant model may be responsive to the sensed wind speed and direction within the finite horizon, the current state of the launch vehicle, and control commands of the launch vehicle, the plant model configured to calculate a future state of the launch vehicle over successive time steps of launch vehicle states using the Newtonian discrete time state space equations in an iterative loop x(k+1)=A(k)x(k)+Bu(k)u(k)+Bv(k)v(k), x(k)=x(k+1), where A(k) is a state transition matrix that describes the free motion of the vehicle and includes coefficients which define the effects of aerodynamic forces on the launch vehicle including the physical quantities of the launch vehicle, Bu(k) is a matrix indicating the relation of control system variables, u(k), which effect a propagated state of the vehicle, Bv(k) is a matrix indicating the relation of wind effect to the propagated state of the vehicle, v(k) is the measurable disturbance, and x(k) is the vehicle state including the attitude and attitude rate of the vehicle and a velocity vector component of the body of the vehicle. The plant model may be configured to calculate the future trajectory of the launch vehicle physical quantities over the finite horizon based upon the sensed wind speed and direction and the calculated future state of launch vehicle using a Newtonian discrete time output state space equation y(k)=C(k)x(k)+Dv(k)v(k) where C(k) is an output matrix which relates how the current state of the vehicle, x(k), is the current and future predicted state of the launch vehicle, Dv(k) is a matrix which indicates how the sensed wind speed from the wind correlator and other measurable disturbances are related to the output of the plant model, and y(k) is an output that forms the input to error circuit and may further include the total vehicle angle of attack, the position, attitude, and flight path angle of the vehicle. The physical quantities of the launch vehicle may include at least one lift, drag, gravity, vehicle mass, vehicle moment of inertia, position of center of pressure of the vehicle, position of center of gravity of the launch vehicle, or current trim conditions. The reference trajectory may be a predetermined trajectory of the launch vehicle. The error circuit may be responsive to the predicted trajectory and the predetermined trajectory. The error circuit may be configured to calculate the trajectory error by computing the difference between the predetermined trajectory and the predicted trajectory.
The predetermined trajectory may be stored in a database which is accessed by the error circuit as a function of time, velocity, acceleration, or position. The optimizer may be responsive to the trajectory error, vehicle constraints, a cost function of the vehicle trajectory, and the current state of the vehicle, the optimizer determining the control commands that approximately minimize the cost function. The optimizer may determine the control commands that approximately minimize the cost function by iteratively comparing costs determined by the cost function of the launch vehicle trajectory using a plurality of different candidate control trajectories within the finite horizon that satisfy the vehicle constraints. The cost function may include the total angle of attack derived from the trajectory error and the candidate control trajectories. The optimizer may be responsive to the trajectory error, the vehicle constraints, the cost function of the vehicle trajectory, and the current state of the vehicle. The optimizer may be configured to calculate the control command which approximately minimizes the cost function using the equation
{overscore (u)}(k)=xe2x88x92Kduxe2x88x921[xe2x88x92{overscore (r)}TWySu+Hv{overscore (v)}WySu+{overscore (x)}TSxTWySu]T where Kdu=K1TWuK1+SuTWySu,             K      1        =          [                                    I                                0                                …                                0                                                I                                I                                …                                0                                                ⋮                                ⋮                                ⋰                                ⋮                                                I                                I                                …                                I                              ]        ,
where I is an identity matrix of suitable size for the production horizon, Wu and Wy are cost function matrixes which includes gains for regulating the performance of the load relief system,             S      u        =          [                                                  CB              u                                            0                                0                                0                                                                              CB                u                            +                              CAB                u                                                                        CB              u                                            0                                0                                                ⋮                                ⋮                                ⋰                                0                                                                              ∑                                  n                  =                  1                                                  p                  -                  1                                            ⁢                              xe2x80x83                            ⁢                                                CA                  h                                ⁢                                  xe2x80x83                                ⁢                                  B                  u                                                                                                        ∑                                  h                  =                  1                                                  p                  -                  2                                            ⁢                              xe2x80x83                            ⁢                                                CA                  h                                ⁢                                  xe2x80x83                                ⁢                                  B                  u                                                                          …                                              CB              u                                          ]        ,
p is an integer that indicated the length of the prediction horizon, t equals p multiplied by dt, wherein t is the length in seconds of the prediction horizon and dt is sampled rate of the control system,             H      v        =          [                                                  CB              v                                                          D              v                                            0                                …                                0                                                              CAB              v                                                          CB              v                                                          D              v                                            …                                0                                                ⋮                                ⋮                                ⋮                                ⋰                                ⋮                                                                              CA                                  p                  -                  1                                            ⁢                              xe2x80x83                            ⁢                              B                d                                                                                        CA                                  p                  -                  2                                            ⁢                              xe2x80x83                            ⁢                              B                d                                                                                        CA                                  p                  -                  3                                            ⁢                              xe2x80x83                            ⁢                              B                d                                                          …                                              D              v                                          ]        ,            and      ⁢              xe2x80x83            ⁢              S        x              =                  [                                            CA                                                                          CA                2                                                                        ⋮                                                                          CA                p                                                    ]            .      
The optimizer calculate a finite horizon optimal control sequence based upon optimization variables xcex1i, ui, xcex4ui that provide future vector control commands. The cost function may be             J      i        =                  ∑                  l          =                      i            +            1                                    i          +          N                    ⁢              xe2x80x83            ⁢              L        ⁢                  xe2x80x83                ⁢                  (                                    α              l                        ,                          u              l                        ,                          δ              ⁢                              xe2x80x83                            ⁢                              u                l                            ⁢                              xe2x80x83                            ⁢              W                                )                      ,
where J is the cost, N the length of the finite horizon within which the optimizer minimizes cost J, xcex1l, ul, xcex4uilxcex5[i, i+1, . . . , i+N] are the optimization variables, and W is a vector of weights or multipliers that scale the variables ul, xcex4ul, xcex1l. The vehicle constraints may define the maximum and minimum allowable value outputs to the physical limits of the vehicle and an actuator of the system. The vehicle constraints may include at least one of the following maximum control surface deflections, maximum vehicle speed, minimum vehicle speed, maximum vehicle acceleration, and minimum vehicle acceleration. The optimizer may be chosen from the group consisting of linear, quadratic optimization problems with linear constraints (LSSOL), linear and nonlinear programming problems (SNOPT), simplex and quasi-Newton algorithms, linear, quadratic programming (QPOPT), linear and nonlinear programs (MINOS), non-convex optimization functions, simplex and quasi-Newton solvers, nonlinear programming based solver with and without constraints (NPSOL), Lmpack software, SPOOLES, and optimal trajectory generation with nonlinear differential dynamics and using NPSOL software. The optimizer may determine the control command which approximately minimizes the cost function using optimization algorithms chosen from the group consisting of steepest descent, gradient descent, conjugate gradient, simplex method, Newton methods including Gauss-Newton, Newton-Raphson, and Fletcher-Powell, Broyden methods, dynamic programming, integer programming, linear programming, nonlinear programming, quadratic programming, linear least squares optimization including Gauss-Newton, Levenberg-Marquardt, and primal-dual family of algorithms. The load relief system of this invention may include a computer system for implementing alone or in combination the wind correlator, the plant model, the error circuit, and the optimizer.
This invention further features a load relief system for a launch vehicle including a wind sensing system responsive to wind speed and direction at selected locations for providing an output of sensed wind speed and wind direction at the selected locations, a plant model responsive to the sensed wind speed and direction within a finite horizon, a current state of the launch vehicle, and control commands of the launch vehicle to predict the future trajectory of the launch vehicle by integrating equations of motion, an error circuit responsive to the predicted trajectory and a reference trajectory to produce a trajectory error, and an optimizer responsive to the trajectory error, vehicle constraints, a cost function, and the current state of the launch vehicle, the optimizer configured to provide control commands to compensate for wind load over the finite horizon of the launch vehicle by determining the control commands that approximately minimize the cost function.
This invention further features a method for providing load relief to a launch vehicle, the method including sensing the wind speed and direction at selected locations, predicting the trajectory of the launch vehicle with a plant model responsive to the sensed wind speed and direction within a finite horizon, a current state of the launched vehicle, and control commands of the launch vehicle, determining a trajectory error by comparing a reference trajectory to the predicted trajectory, and generating from the trajectory error, control commands to compensate for wind load at the finite horizon of the launch vehicle by reducing the total angle of attack.
In a preferred embodiment, the wind sensing system may be directed from the launch vehicle. The wind sensing system may be directed from a launch platform proximate the launch vehicle. The wind sensor may be directed from an aircraft.